Pyspark过滤器使用从列表开始

Question

我有一个元素列表,可能会启动一些RDD记录的字符串.如果我有和元素列表的yes和no,它们应该匹配yes23,no3但不是35yes或41no.使用pyspark,我如何使用startswith列表或元组中的任何元素.

DF的一个例子是:

+-----+------+
|index| label|
+-----+------+
|    1|yes342|
|    2| 45yes|
|    3| no123|
|    4|  75no|
+-----+------+

当我尝试:

Element_List = ['yes','no']
filter_DF = DF.where(DF.label.startswith(tuple(Element_List)))

生成的df应该类似于:

+-----+------+
|index| label|
+-----+------+
|    1|yes342|
|    3| no123|
+-----+------+

相反,我得到错误:

Py4JError: An error occurred while calling o250.startsWith. Trace:
py4j.Py4JException: Method startsWith([class java.util.ArrayList]) does not exist
at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:318)
at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:326)
at py4j.Gateway.invoke(Gateway.java:272)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:745)

提示,因此看起来startsWith不能与任何类型的列表一起使用.有简单的工作吗？

Answer 1

像这样撰写表达式:

from pyspark.sql.functions import col, lit
from functools import reduce

element_list = ['yes','no']

df = spark.createDataFrame(
    ["yes23", "no3", "35yes", """41no["maybe"]"""],
    "string"
).toDF("location")

starts_with = reduce(
    lambda x, y: x | y,
    [col("location").startswith(s) for s in element_list], 
    lit(False))

df.where(starts_with).show()
# +--------+
# |location|
# +--------+
# |   yes23|
# |     no3|
# +--------+